Voice activity detection (VAD) is generally a technique for detecting a voice activity in a signal. Voice activity detectors are widely used in the telecommunication field. A basic function of a voice activity detector is to detect, in communication channels, the presence or absence of active signals, such as speech or music signals. The voice activity detector can be provided within a communication network, wherein the network can decide to compress transmission bandwidth in periods where active signals are absent, or to perform other processing depending on a voice activity detection decision (VADD) indicating whether there is an active signal or not. A voice activity detector can compare a feature parameter or a set of feature parameters extracted from the input signal to corresponding threshold values, and determine whether the input signal includes an active signal or not based on the comparison result. The performance of a voice activity detector depends to a high degree on the choice of the used feature parameters.
There have been many feature parameters proposed for voice activity detection, such as energy based parameters, spectral envelope based parameters, entropy based parameters, higher order statistics based parameters and so on. In general, energy based parameters provide a good voice activity detection performance. In recent years, sub-band signal to noise ratio (SNR) based parameters as a kind of energy based parameters have been widely used in the telecommunication field. In sub-band SNR based voice activity detectors, the SNR for each frequency sub-band of an input frame is detected, and the SNRs of all sub-bands are added to provide a segmental SNR (SSNR). The SSNR can be compared with a threshold value to make a voice activity detection decision (VADD). The used threshold is usually a variable, which is adaptive to a long term SNR (LSNR) of the input signal or a level of background noise.
In a recently completed ITU-T (International Telecommunication Union Telecommunication Standardization Sector) Recommendation G720.1 (G720.1 hereinafter), the conventional SSNR parameter has been improved by applying a non-linear processing to get a modified SSNR (MSSNR). The calculated MSSNR is also compared to a threshold which is determined from a threshold table according to the LSNR of the input signal, the background noise variation and the voice activity detection (VAD) operating point, where the VAD operating point defines the tradeoff of the VAD decision between active and inactive detection, for example a quality-preferred operating point will make the VAD favor the detection of active signals and vice versa.
Although the MSSNR parameter used by G720.1 does increase the performance of the voice activity detection, the VAD performance in a non-stationary and low SNR background environment still needs improvement. Conventional voice activity detectors are designed to balance their performances in various background noise conditions. Accordingly, conventional voice activity detectors have a performance which is sub-optimal for specific conditions and in particular in a non-stationary and low SNR background environment.